Inferring Genetic Relationship Among Haplomes in Triticeae: The Utility of the 5S DNA Units with Examples

Size: px
Start display at page:

Download "Inferring Genetic Relationship Among Haplomes in Triticeae: The Utility of the 5S DNA Units with Examples"

Transcription

1 Inferring Genetic Relationship Among Haplomes in Triticeae: The Utility of the 5S DNA Units with Examples B. R. B Environmental Health program, Biodiversity Theme, Agriculture & Agri-Food Canada, Neatby Building, Ottawa, Ontario, Canada, K1N 9M5, baumbr@agr.gc.ca Abstract: In higher plants nuclear rrna is encoded by multiple copies of rdna genes, arranged in arrays of tandem repeats at one or more loci. Each unit comprises a coding region of ca. 120 bp and a non-transcribed spacer (NTS) that contains regulatory signals for transcription. In the Triticeae two separate major loci containing tandem arrays coexist, differentiated in the main by the length and nucleotide sequence of the NTS. Such groups of sequences were named unit classes. Unit classes, alone or in combination, and the differences between them are useful in representing haplomes of taxa within the Triticeae. Moreover, phylogenetic relationships among unit classes and the haplomes that they signify may be inferred from the nucleotide sequences of the NTS. Several examples will be presented and discussed along with some issues relating to multigene families and phylogenetic inference. Keywords: cloning; sequence alignment; maximum likelihood; Bayesian analysis; evolutionary inference; unit classes; twat of orthology The 5S DNA gene codes for the 5S ribosomal portion. The 5S DNA units in the Triticeae are organized in arrays of tandem repeats with the highly conserved genes separated by the more variable, non-transcribed spacer region (henceforth NTS). In a number of publications (e.g., B & B 1997, 2000, 2001; B & J 1994, 1996, 1998, 1999, 2000, 2002, 2003; B et al. 2001, 2003), we have described the molecular diversity of 5S DNA sequences in species within the genera Elymus, Hordeum, Kengyilia, and Triticum and based on their sequences classified the 5S DNA units into putative orthologous groups, which we called unit classes. In addition we found that we could assign the different unit classes to haplomes. For example, in H. vulgare L. we found sequences belonging to a unit class we labelled short I1 to represent the I haplome identified in this taxon. Subsequent analyses (ibid) led to the assignment of unit classes to the other haplomes or four basic genomes in Hordeum (B et al. 1986, 1987). Studies by A and B (A & B 1992; B & A 1992) tentatively divided the 5S rrna genes in the Triticeae into two types the short type ranging in size from 327 to 468 base pairs (bp) and the long type ranging from bp and we initially adopted their terminology. As described in our previous publications (ibid.), duplications, insertions and/or deletions can have a profound effect on this simple division. The effect is actually so profound that the length alone does not necessarily determine the unit class or the assignment of a sequence to a particular unit class and haplome. It is the sequence divergence and the pattern of blocks of sequences that are crucial for their correct assignment (B et al. 2001). Most taxa that have been investigated to date contain two unit classes per haplome but several exceptions have been identified. For instance in bread wheat, a hexaploid, we found five unit classes assignable to the three haplomes (B & B 2001), the expected number would have been six; it may be that we failed to capture the sixth. We are able to use the sequence divergence of the 5S DNA NTS, and the assignment of unit classes to haplomes, Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

2 to infer phylogenetic relationships among the various haplomes. MATERIALS AND METHODS Cloning and sequencing. The materials investigated and the isolation of genomic DNA, PCR amplification of the 5S DNA genes, cloning of PCR products and sequencing of plasmid DNA have been described, e.g., in B and B (1997, 2000, 2001), B and J (1994, 1996, 1998, 1999, 2000, 2002, 2003), B et al. (2001, 2003, 2005). The PCR primers target the coding regions in tandem repeats and amplify a sequence starting from 5' from the BamH1 site within the transcribed region, through the NTS, to a site 3' of the BamH1 site within the adjacent unit in the array. Amplimers were either digested with BamH1, cloned into the BamH1 site of puc19 (Y -P et al. 1985), and transformed into Escherichia coli strain DH5α or latterly ligated directly into pgem-t Easy (Promega Biotech) and transformed into DH5α. For each sequence from hundreds of clones, both strands were sequenced. Determination of putative orthology. Alignments and manual refinement were carried out as detailed in B et al. (2001) to determine unit classes, i.e. putative orthologous groups. Based on BLAST (basic local alignment search tool, A et al. 1990) searches, these classes were labeled to reflect known haplomes in Triticeae. Test of orthology. To test for orthology of the units within unit classes Maximum Likelihood (ML) analysis was carried out using fastdnaml (O et al. 1994). The results have also been used to assess diversity among the units within each unit class. Prior to the ML analysis the alignments were subjected to likelihood ratio tests of 56 different evolutionary models (P 2003; F 2004) to choose the best fitting model and parameters given the data in conjunction with PAUP (S 1998) version 4.0b10 and using MODELTEST (P & C 1998). Phylogenetic inference among unit classes. Long H1, short I1, long H2 and long Y2 unit classes in Hordeum. First, selection of exemplar sequences was made from among the putative orthologous sets of sequences; ML analyses using PAUP and Bayesian analyses using MrBayes (H & R 2001) were then carried out. Haplome relationships in Triticeae: Example (1) Unit classes in Hordeum. Unit classes data were first summarized based on their presence/absence within taxa. A neighbor-joining (S & N 1987) analysis (NJ) was then performed and the tree was then rooted by a hypothetical ancestor, the choice of which is discussed below. The rooted tree was then subjected to a tree analysis using parsimony in order to obtain and describe the unit class changes on the NJ tree. Haplome relationships in Triticeae: Example (2) Secale and related haplomes in Triticeae. Conducted as in 4 above, i.e. by selecting exemplar sequences first and then conducting ML and Bayesian analyses. RESULTS AND DISCUSSION Long H1 and short I1 unit classes in Hordeum vulgare Results from our initial analysis of a large numbers of clones isolated from Hordeum vulgare L. showed that the two classes recommended by A and B (1992), i.e., the long and the short types, vary so much in size that there is a substantial overlap between the two. For example the alignment in Figure 1 depicts the two unit classes and displays the variation between them. In this example some of the short units are actually longer than some long units in part due to the presence of (TAG) repeats within the NTS of the short units. In several publications we have extensively documented the effects of duplications, insertions and/or deletions of the length of the NTS that render the simple division of units into short and long difficult. Furthermore, the combination of these two unit classes was found to be characteristic of the species containing the I haplome, viz. Hordeum vulgare, H. spontaneum C. Koch, and both diploid and tetraploid H. bulbosum L. (B & J 1996). The naming of the unit classes reflects the haplomes. Thus the short I unit class in this example is characterized by a contiguous chain of two to many TAG repeats (the top sequences in Figure 1). We found that the establishment, and thus recognition of classes, depends on the pattern of the sequences which may be revealed by careful alignment. We will return to the problem of the recognition of classes in the section Detection of Orthology below, but these results suggest that the sequences of the different NTSs can be used to define unit classes and to determine relationships among haplomes and species of the Triticeae. 40 Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

3 HVUL014 : Kolchinsky : HVUL015 : HVULLO3 : HVUL017 : HVUL026 : HVUL012 : HVUL021 : HVUL033 : HVUL011 : HVULLO4 : HVULL05 : HVULL06 : HVULL07 : HVULS01 : HVULS02 : HVULL01 : HVULL02 : HVUL023 : * 20 * 40 * 60 * 80 * 100 * 120 * TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAGCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAGCGTGATCTATATGACCTCATTTACTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGCGACCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGG-AGCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACCTGACAAGGATGACGCGGGAGCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAACGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAGCGTGATCTATATGACTTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAGCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAAATATATTTTTGCGCCACGTGACAAGGATGACGCGGGAGCGTGATCTATATGACCTCATTTTCTTATTTTTGACGATT TTTTTAATAAATTTTTGCTCCGCGCGAGAAACGATGACGCACGTGCGCGGTATTTATTAA ACAATGTTTCTTATTTTTGGCGTTTGCGGTAAGTTATAGCTTGCGGCGCG -TTTTTAACAAATTTTTGCTCCGCGCGAGAAACGATGACGCACATGCGCGGTATTTATTAA ACAACGTTTCTTATTTTTGGCGTTTGCGGTAAGTTATAGCTTGCGGCGCG TTTT AAtA TTTTTGC CCaCg GA AA T : 83 : 110 : 110 HVUL014 : Kolchinsky : HVUL015 : HVULLO3 : HVUL017 : HVUL026 : HVUL012 : HVUL021 : HVUL033 : HVUL011 : HVULLO4 : HVULL05 : HVULL06 : HVULL07 : HVULS01 : HVULS02 : HVULL01 : HVULL02 : HVUL023 : 140 * 160 * 180 * 200 * 220 * 240 * ACTGTGTGACTTTTTCCACCGCGCTTGACACCCA GTACTCACGCGTCTAGGG-CGGCGTTGCGGTGGCAAAGGTAGCGCGTTGTGAAAGGGGTCGAAACCG-TGGTAGAACTCGTGTTGGTGCGGTAGAGAGGGAAGGGTGGAAACG--GTGGAAAGC-CCGTCTTTG GTACTCACGCGTCTAGGGGCGGCGTTGCGGTGGCAAAGGTAGCGCGTTGTGAAAGGGGTCGAAACCGGCGGTAGAACTCGTGTTGGTGCGGTAGAGAGGGAAGGGTGGAAACG--GTGGAAAGC-CCGTCTTTG GTACTCACGCGTCTAGGGGTGGCGTTGCGGTGGCAAAGGTACCGCGTTGTGAAAGGGGTCGAAACCG-CGGTAGAACTCGTGTTGGTGCGGTAGAGAGGGAAGGGTGGAAACG--GTGGAAAAC-CCGTCTTTG A G GA C : 117 : 241 Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

4 HVUL014 : Kolchinsky : HVUL015 : HVULLO3 : HVUL017 : HVUL026 : HVUL012 : HVUL021 : HVUL033 : HVUL011 : HVULLO4 : HVULL05 : HVULL06 : HVULL07 : HVULS01 : HVULS02 : HVULL01 : HVULL02 : HVUL023 : HVUL014 : Kolchinsky : HVUL015 : HVULLO3 : HVUL017 : HVUL026 : HVUL012 : HVUL021 : HVUL033 : HVUL011 : HVULLO4 : HVULL05 : HVULL06 : HVULL07 : HVULS01 : HVULS02 : HVULL01 : HVULL02 : HVUL023 : * 280 * 300 * 320 * 340 * 360 * 380 * 400 ACGACTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG ACGACTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG ACCACTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG ACGACTAGTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG ACGACTAGTAGTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCA ACGACTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAG GGGCAA-CATAAGGAACAAATAGAT AGTT--GCATGTCG ACGACTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTA GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG AGGACTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG ACGACTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAGTAG GGGCAAGCATAAGGAACAAATAGAT AGTT--GCATGTCG TTGTTGAGCGGGAGAGTACGTGGTACGGTGCGGTATCCGTTATTAGGAGCGGTGAAAAAGAATGTACGGAGGTGTTTATGGTGGAGCTGAGAGGGGCTAGAATAAGGGACGAAGGCGGGA-GT-AA-CATGTCG TTGTAGAGCGGGAGAGTACGTGGTACGGCGCGGTATCCATTATTAGGA TTGTTGAGCGGGAGAGTACGTGGTACGGTGCGGTATCCGTTATTAGGAGCGGTGGAAAAGAATGTACGGAGGTGTTTATGGTGGAGCTGGGAGGGGCGAGAATAAGGGACGAAGGCGGGGAGTA--ACATGTCG g AG G AG gt gta g g g a c agg c a a a gt c tgtcg Transcription>>> BamH1 * 420 * 440 * 460 * 480 * 500 * 520 GATGCCATCATACC-AGCA-CTAAAGCACCGGATCCCATCAGAACTTCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATACC-AGCA-CTAAATCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGTGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATACC-AGCA-CTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATAGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATAAC-AGCA-CTAAACCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC AATGCGATCATACC-AGCA-CTAAAGCACCGGATCCCATCAAAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATACCCAGCAACTAAAGCACCGGATCCCATTAGATCTTCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTACCTCGTGTTGCATTCCCC GATGCGATCATACC-AGTA-CTAAACCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATAGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATACC-AGCA-CTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTTCCC GATGCGATCATACC-AGCA-CTAAAGCACCGGATCCCATCAGAACTTCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCCCC GATGCGATCATACC-AGCA-CTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTAGGAAGT-CCTCGTGTTGCATTCTC CTAAAGCACCGGATCCCATCCGAACTCCGAAGTTAAGCTTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCTC- GATGCGATCATACC-AGCACTTAAAGCACCGGATCCCATCAAAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATAGGTGACCTCCTGGGAAGT-CCTCGTGTTGCATTCTCgatgc atcatacc agca T AAgCACCGGATCCCATcagAaCTcCGAAGTTAAGC TGCTTGGGcGAGAGTAGTACTAGGATgGGTGACCTCCTgGGAAGT CCTCGTGTTGCATTc C : 292 : 301 : 301 : 303 : 307 : 315 : 315 : 322 : 340 : 390 : 491 : 172 : 181 : 181 : 183 : 187 : 192 : 195 : 202 : 220 : 289 : 371 Figure 1. Alignment of selected 5S DNA sequences in H. vulgare depicting the differences between two putative unit classes: the short I1 (first 10 sequences from the top) and the long H1 (the remaining sequences). The likely start site for transcription and BamH1 site are noted 42 Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

5 Long H1, short I1, long H2 and long Y2 unit classes in Hordeum The South American diploid Hordeum species belong to the HH genome species (B & J 1991). Based on 374 sequences of 12 taxa we found that two different unit classes characterize them, viz the long H2 and long Y2 (only a small fraction of the alignment in shown in Figure 2, where the two unit classes differ obtained after deleting the sequences from top and from bottom, for illustration only). Based upon LRT tests, the data best fit the HKY+G, i.e. the Hasegawa model (H et al. 1985) with the Gamma distribution rates of nucleotide substitutions (Y 1994). ML analyses and various tests including the molecular clock, as well as Bayesian evolutionary inference analysis implied that the long H1 and short I unit classes found in the II genome diploids diverged from each other at the same rate as the long H2 and long Y2 unit classes found in the HH genome diploids (Figure 3). The divergence among the unit classes, estimated to be circa 7 MY, suggests that the genus Hordeum may be a paleopolyploid (B et al. 2005). Figure 3 also depicts the test of orthology (see Discussion). Once more these results suggest that analysis of the NTS can be useful for investigating relationships between haplomes in Hordeum. Haplome relationships in Triticeae: example (1) Unit classes in Hordeum The resulting NJ tree (Figure 4) is shown with the inferred unit class changes. This tree was rooted at a hypothetical ancestor containing both the long H1 and a long Y2 unit class, as no outgroup was contemplated. The key point here is that results based on the analysis of 5S DNA unit classes could be used to infer the evolutionary path among the Hordeum taxa and could bear directly on the relationship among the haplomes in the genus. An example of our analysis of the Triticeae tribe based upon the results from the time calibration analysis was recently presented (B et al. 2005). Hordeum as we know it today was most likely different from the ancestral stock that may have originated at about the start of the drift of Africa from South America (at start of the Cretaceous). The discovery that H. capense (S. Africa) and H. depressum (N. America) are the only extant species found to contain both the long H1 and long Y2 unit classes provides support for this hypothesis; subsequent analysis of the DMC1, EF-G and rbcl genes by P and S (2004), also supports the idea that H. capense is an ancient relict. The several more Hordeum species currently being investigated may help solidify this interpretation. Haplome relationships in Triticeae: example (2) Secale and related haplomes in Triticeae The Secale sequence analysis identified two unit classes, the long R1 and short R1. The test of orthology of these unit classes is depicted in the ML tree (Figure 5). A BLAST search for 5S DNA sequences from known unit classes most closely similar to the long R1 unit class contained sequences of the long P1 unit class from Agropyron (PP haplomes) and from Kengyilia (StStYYPP haplomes), long J1 from Thinopyrum (J haplome), whereas the search for sequences of the short R1 included the long S1 from Pseudoroegneria (St haplomes) and Kengyilia (StStYYPP haplomes), the short J1 from Thinopyrum (J haplomes) and the short V1 from Dasypyrum (V haplomes). ML and Bayesian analyses yielded a tree with the long R1 units where the long P1 and long J unit classes were closest to the R1 unit class, whereas they yielded a tree with the short R1 units where the S1 and short J1 unit classes were closest to the short R1 unit class. This result indicates a possible close relationship between the St, J and R haplomes (not shown) and again indicates how analysis of the NTS can be used for formulate hypotheses for future study. Detection of orthology Determination of unit classes. Central to this discussion is the detection of orthologous groups of sequences and their grouping into unit classes. The determination of putative orthologous groups of sequences is much more advanced for protein than for DNA sequences in part because orthology analysis is becoming an important aspect of gene function prediction. The use of phylogenetic information in genome annotation is known as phylogenomics (E 1998). Conventional phylogenomics methodology employs mostly manual approaches; however, recent advances have been made in automating protein phylogenomics, based on similarity clustering, such as the COGs database (T et al. 2001). Recently, attempts have been made to use explicit phylogenetic tree Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

6 HSET011 : HSET016 : HSET022 : HSET030 : HSET032 : HSET035 : HSET039 : HSET040 : HSET041 : HMAG001 : HMAG003 : HMUS0013 : HMUS0020 : HMUS009 : HMUS0011 : HMUS001 : HMUS0012 : HMUS004 : * 20 * 40 * 60 * 80 * 100 * 120 * 140 * 160 * 1 -TTTTTAATATATTTTTGCTCCACACGAGAAACATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTTCGA--CGTTTT-C-GGTAAGTTTT--AGCTT-G-CTGGTCTTTATCC--A-CGGGTGTAGGG-CGTCGTTGCGGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGA--CTTTTT-C-GGTAAGTTT--TAGCCT-G-CGGATCATTATCC--A-CGCATGTAG-GGCGGCGTTGCTGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTTTTAATATATTTTTGCTCCACGCGAGAAACATACGTACGTGCGCGGAATATATTAAGCACGGTTTCTTATTTTCGA--CGTTTT-C-GGTAATTTT--TCGCTT-G-CGGGTCATTATTC--A-CGCGTGTAGGG-CGGCGATGCGGTGGCAAAGGTACCGGGTT---CTGAAAG -TTTTTAATATATTTTTGATCCAAGCGAGAAACATGCGTACGTGCGCGGAATATATTAAGCGCGGTTCCTTATTTTTTA--CGTTTT-C-GGCAAGTTT--TAGCTT-G-CGGGTCATTATCC--A-CTCGTTTACGGGCGTCGTTGCGGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTTTTAATATATTTTTACTCCACGCGAGAAACATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTTTGA--CGTTTT-C-GTTAAGTTC--TAGCTT-G-CGGGTCATTATCC--A-CGCATGTAGGGGCGTCGTTGCGGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTCATAATATATTTTTGCTCCACGCGAGAAACATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTTCGA--CGTTTT-T-GGTAAGTTC--TAGGTT-G-CGGGTCATTATCC--A-CGCGGGTAGGGGCGTCGTTGCGGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTTCTAATATATTTTTGCTCCACGCGAGAAACATGCGTCCGTGCGCGGAATATATCAAGCACGGTTCCTTATTTTCGA--CGTTTT-C-GGTAAGTTT--TAGCTT-G-CGGGTCATCATCC--A-CACGTGTAGGGGCGTCGTTGCGGTGGAAAAGTGGTCGGGTA---CTGAAAG -TTTTTAATATATTTTTACTCCACGCGAGAAACATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTTCAA--CGTTTC-C-GGTAAGTTT--TAGCTT-G-CGGGTCATTATCC--A-CGCGTGTAGGGGCGTCGTTGCGGTGAAAAAGTGGTCGGGTT---CTGAAAG -TTTTTAATATATTTTTGCTCCACGCGAGAAACATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTTCGA--CGTTCT-C-GGCAAGTTT--TAGCTT-G-CGGGTCATTATCC--A-CGCGTGTAGGGATGTCGTTGCGGTGGAAAAGTGCTCAGGTA---CTGAAAG -TTTTTAATATATTCTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCTGACTTCTCGTGCTGATCAGTTTGATATATTATACG--TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT TTTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACG--TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTTTCATTACACGTTTTT -TTTTTAATATATTTTTGATCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGTACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGT-TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTCTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGG-TTTTTTTCGACATGATATGCAAAA--CTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGG-TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGG-TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACCCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGG-GTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGCTGATCAGTTTGATATATTATACGG-GTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT -TTTTTAATATATTTTTGCTCCACGCGAGAAAGATGCGTACGTGCGCGGAATATATTAAGCACGGTTCCTTATTTCCGACTTCTCGTGTTGATCAGTTTGATATATTATACGG-TTTTTTTCGACATCGTATGCAAAA--GTTATAGCCGTT--TTACTTTTT-CATAACACTTTTTT TTttTAATATATTtTTgcTCCAcgCGAGAAA ATgCGTaCGTGCGCGGAATATATtAAGcaCGGTTcCTTATTT cga T t c G t AgTTt ta tt Cgg t Tt Tc A cg tg A gt t GC GT A t t Ta Ct : 162 : 162 : 162 : 163 : 163 : 163 : 163 : 163 : 163 : 170 : 172 HSET011 : HSET016 : HSET022 : HSET030 : HSET032 : HSET035 : HSET039 : HSET040 : HSET041 : HMAG001 : HMAG003 : HMUS0013 : HMUS0020 : HMUS009 : HMUS0011 : HMUS001 : HMUS0012 : HMUS004 : 80 * 200 * 220 * 240 * 260 * 280 * 300 * 320 * 340 * GGGTCAAAACCGCGGTAAAACTCTA-GTTGGTGCGGTAGAG-AGGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGCTACGGTGCAGTATTCATTATTAGGCAGCGTTGGCAAAGAGTGCTCGATCTTGTTTGTGGTGGAGCCGGGA-- GG-TCGAAACCGCGATAAAACTCTA-GTTGATGCGGTAGAG-AGGGAGGGGTGGAGACCTTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTACGGTGCAGTATTCGTTATTAGGCAGCGTTGGCAAAGAGTGCCCGATCATGTTTGTGGTGGAGCCGGGA-- GGGTCGAAAACGTGGTAAAACTCTT-GTTGGTGCGGTAGAC-AGGGAGGGGTGGAGAACGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTATGTGGTACGGTGCAGTATTCGTTATTAGGCAGTGTTGGCAAAGAGTGCTCGATCGTGTTTGTGGTGGAGCCGGGAA- GGGTCGAAACCGCGGTAAACCTCTA-GTTGGTGCGGTAGAG-AGGGAGGGGTGGAGACCATGGTAAACATGACACGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTATGGTGCAGTATTCGTTATTAGGAAGCGTTGGCAAAGAGTGCTCGGTCGTGTTTGTGGTGGAGCCAGGA-- GGGTCGAAACCGCGGGAAAACT----GTTAGTGCGGTAGAG-AGGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTACGGTGTAGTATTTGTTATTATGCAGCGTTGGCAAAGAGTGCTCGATCATGTTTGTGGTGGAGCCGGGA-- GGGTCGAAACCGCGGTAAAACTCTA-GTTGGTGCGGTAGAG-AGGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTACGGTGCAGTATTCGTTATTAGGCAGCGTTGACAAAGAGTGCTCGATCGTGTTTGTGGTGGAGCCGGGA-- GGGTCGAAACCGCGGTAAAACTCTA-GTTGGCGCGGTAGAG-ACGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTACGGTGCAGTATTCGTTATTAGGCAGCGTTGGCAAGGAGTGCCCGATCGTGTTTGTGGTGTAGCCGGGA-- GGGTCGAAACCGTGGTAAAACTCTA-GTTGGTGCGGTAAGG-AGGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACGTTGTTG-AGCGGGAGAGTAAGTGGTACGGTGCAGTATTCGTTATTAGGCAGCGTTGGCAAAGAGTGCTCGATCGTGTTTGTGGTGGAGCCGGGA-- GGGTCGAAACCGCGGTAAAACTCTA-GTTGGTGCGGTAGAA-AGGGAGGGGTGGAGACCGTGGTAAACA-----CGTGTACATTGTTG-AGCGGGAGAGTAAGTGGTACGGTGCAGTATTCGTTATTAGGCAGCGTTGGCAAAGAGTGCTCGATCGTGTTTGTGGTGGAGCCGGGA-- G---CAAAACAT--GTCAAAATTTGTGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAAGACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATAAATT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATTA---GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATACAG-GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TAAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTCT-CAAAACTAAAATGGCGATA-----GGG--G G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATACAG--GGG--G G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATAGGG-GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATAGGG-GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATAGGG-GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAGTTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATAGGG-GGGG--- G---CAAAACAT--GTCAAAATTTATGTTTTTGAATTTTCCTA---ACTAAT--AGAT-GTAGTAA-CA-----TAAATACAT-CTCGAAG---GATT-TTAATTTT----TGAAGTTTTTATCATTT----TCTTTT-CTTTTTTT-CAAAACTAAAATGGCGATAGGG-GGGG--- G C AAAc gt AAa T ta GTT tg T A A T AGA gt GTAA CA tac T T G AG GA T a T T TG AGT TT T AtT c TT C T C a T G G T g ggg : 330 : 329 : 331 : 336 : 328 : 331 : 331 : 331 : 331 : 310 : 314 : 310 : 313 : 313 : 313 : 313 : 313 : 313 HSET011 : HSET016 : HSET022 : HSET030 : HSET032 : HSET035 : HSET039 : HSET040 : HSET041 : HMAG001 : HMAG003 : HMUS0013 : HMUS0020 : HMUS009 : HMUS0011 : HMUS001 : HMUS0012 : HMUS004 : 360 * 380 * 400 * 420 * 440 * 460 * 480 * 500 * 520 GGGG--CGAGCATAATGGACGAAGGCGGGGCGTAACATGTCGGATGC-GATCATACTAGC---ACTAAAACACCGGATCCCATAAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCATGTTGCAT-TCCC GGGG--CGAGCATAATGGACGAAGGCGGGT-GTAACATGTTGGATGC-GATCATACCAGC---ACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGAGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC GGGG--CGAGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATACCAGC---ACTAAAGCATCGGATCCCATCAGAATGTTGAAGTTAAGCGTGCTTGGGCTAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCG GGCG--GGAGCATAATGGACGAAGGCGGGG-GAAGCATGTCGGATGC-GATCATACTAGC---ACTAAAGCACCGGATCCTATCAGAACTCCGAAGTTAAGCGTGCTTGGACGAGAGTAGTACTAGGATGGGTGACCTCTTGGGAAGTCCTCTTGTTGCAT-TCCC GGGG--CGAGCGTAATGGACGAAGGCGGGG-GTAACATGTTGGATGC-GATCATACCAGC---ACTAAAGCACCGGATCCAATCAGAACTTCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCTTGGGAAGTCTTCGTGTTGCAT-TCCC GGGG--CGAGCATAATGGACGAAGACGGGG-GTAACATGTCGGATG-TGATCATACCAGC---ACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGG-TGACCTCCTAGGAAGTCCTCGTGTTGCAT-TCCC GGGG--CGAGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATG-TGATCATACCAGC---ACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAATCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC GGGG--CCAGCATAATGGACGAAGGCGGGG-GTAACAAATTGGATGC-GATCATACAAGC---ACTAAAGCACCGGATCCAATCAGAACAACGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCATTTTC- GGGG--CGAGCATAATGGACGAAGCCGGGG-GTAACATGTCGGATGC-GATCATACCAGC---ACTAAAGCACCGGATCCCATCAAAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGACGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAA-TCCC -GGG--C-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCATGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTAGGAAGCCCTTGTGTTGCAT-TCCC -GGG--C-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCC- GGGG-GC-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGTTGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTTGTGTTGCAT-TCCC GGGG-GC-AGCATAATGGATGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCAAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTTGTGTTGCAT-TCCC C-AGCATAATGGACGAAGGCGGGG-GTAACATGACGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC C-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC -GGT----AGCATAATGGACGAAGGCGGGG-GCAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCCGGGAAGTCCTCGTGTTGCAT-TCCC -G----C-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATTAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGTAGTACTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC -GGGGGC-AGCATAATGGACGAAGGCGGGG-GTAACATGTCGGATGC-GATCATAC---CAGCACTAAAGCACCGGATCCCATCAGAACTCCGAAGTTAAGCGTGCTTGGGCGAGAGAAGTATTAGGATGGGTGACCTCCTGGGAAGTCCTCGTGTTGCAT-TCCC ggg c AGCaTAATGGAcGAAGgCGGGg GtAaCAtgtcGGaTGc GATCATAC C ACTAAAgCAcCGGATCCcATcAgAActccgAAGTTAAgCgtGCTTGGgCgAGAGtAGTAcTAGGAtGGgTGACCTCctgGGAAGtCcTcgTGTTGCAt TcCc : 487 : 494 : 486 : 488 : 466 : 469 : 468 : 471 : 466 : 466 : 468 : 467 : 471 Figure 2. Window in the alignment of 374 units found in the South American native diploid Hordeum species, at the demarcation line between the long H2 (top 9) and the long Y2 (bo om 9) unit classes sequences. Both unit classes are present in all the species, however it is by coincidence that in this window the long H2 units are from H. setifolium and the long Y2 units are from H. patagonicum ssp. magellanicum 44 Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

7 13 MY HSPOS01 HVUL011 HBULL07 HVUL HBUL MY 100% ( , ) 38 (20-38) 7.8 MY 96% HMAG015 HERE030 HSTE039B HSET032 HCHI011 HMUT034 HSAN041 HCOM007 HMUS0019 HPUB023 HPAT011 HCOR ( , ) 23 (15-34) 6.8 MY 100% ( , ) 97 (25-107) 1 MY 100% ( , ) 56 (39-115) 100% ( , ) 63 (37-88) HSAN033 HPUB039 HSET015 HCOM012 HMUT022 HPAT045 HSTE015 HCHI028A HCOR022 HMAG005 HMUS0011 HERE MY HVUL033 HBUL033 HSPO013 HSPO012 HBUL032 HVUL Long H1 Long H2 Long Y2 Short I1 Figure 3. Best maximum likelihood tree, the molecular clock, parsimony trees superimposed on each other, of exemplars of the following unit classes in Hordeum, long H1, short I1, long H2 and long Y2. The first two are found in the I haplome species whereas the last two in the South American diploid H haplome species. MY Million Years since divergence; values above major branches bootstrap support (%); values below major branches: branches lengths (distance from root, distance from tip) and values further below assigned branch length under the parsimony criterion (Min. possible length Max. possible length); scale bar distance from minimum evolution distances; on the right the four unit classes. For example, in the most basal branch: major branch length = (distance from root = , distance from tip = 0,09531); and further below on the same branch: branch length = 38 base changes (minimum length changes = 20 and maximum branch changes = 38) analysis instead to place or classify a sequence in a subfamily or group of a gene tree of known sequences. See for example the protein sequence analyses of Z and E (2002) and A et al. (2003). Conventionally in protein analysis one uses BLAST or similar programs. When one encounters a sequence that is not similar to a previously known group a new group is created. We Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

8 Hypothetical ancestor H. pusillum H. brachyantherum long H2 H. californicum H. chilense Loss long H1 H. comosum H. cordobense H. erectifolium H. euclaston H. flexuosum H. intercedens H. muticum H. patagonicum magellanicum H. patagonicum mustersii H. patagonicum patagonicum H. patagonicum santacrusense H. patagonicum setifolium long H1 long Y2 H. pubiflorum H. stenostachys H. roshevitzii H. vulgare long H3 Loss long H3 short I1 H. bulbosum H. spontaneum long X2 Loss long Y2 Loss long H1 long X1 H. secalinum long X2 short Y1 H. leporinum H. marinum H. glaucum long Y1 H. leporinum simulans H. murinum H. capense H. depressum 0.1 Figure 4. Relationships among all the unit classes thus far investigated in the genus Hordeum. The scale bar indicates the distance in the NJ tree. The unit classes were obtained from the analysis of the NJ tree by maximum parsimony of the unit class presence/absence data. See text 46 Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

9 9strictum 22cereale 48cereale 28cereale 15cereale 110strictu 102strictu 36cereale 49cereale 45cereale 34cereale 51cereale 30cereale 39cereale 37cereale 13cereale Add5cereal 62cereale Add4cereal 35cereale Add8cereal 109strictu 72vavilovi 9cereale 87sylvestr 88sylvestr 4cereale 98strictum 6cereale 84sylvestr Add2cereal 70vavilovi 71vavilovi 73vavilovi 68vavilovi 91strictum 90strictum 94strictum 92strictum 106strictu 32cereale 14strictum 111strictu 43cereale 10cereale 18cereale 8cereale 55cereale 7cereale 58cereale 29cereale 99strictum 56cereale 20cereale 57cereale 44cereale 42cereale 46cereale 19cereale 67vavilovi 60cereale Add11stric Add12stric Add10stric Add13stric 93strictum 21cereale 64vavilovi 14cereale 97strictum 101strictu 79sylvestr 74vavilovi 80sylvestr 78vavilovi 83sylvestr 81sylvestr 75vavilovi 77vavilovi 76vavilovi 33cereale 12cereale 26cereale cereale 112strictu 108strictu Add16stric 65vavilovi Add15stric 107strictu 104strictu Add17stric 100strictu 24cereale 66vavilovi 69vavilovi 86sylvestr 40cereale 11cereale 95strictum 61cereale 82sylvestr 59cereale 105strictu 5cereale 17cereale 16cereale 3cereale Add7cereal 31cereale Add1cereal 23cereale 41cereale 25cereale Add6cereal 63cereale 85sylvestr 2cereale 1cereale Add3cereal 38cereale 47cereale 27cereale 103strictu 50cereale 52cereale 53cereale Figure 5. Test of orthology of the two unit classes in Secale. The sequences at the far right on the long branch were assigned to the long R1 unit class, whereas the le at the base of fastdnaml tree were classed as the short R1 unit class Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

10 have taken the same manual and conventional approach to determine unit classes in the Triticeae (B et al. 2001), except that we validate them a posteriori by phylogenetic analysis (as illustrated in Figures 3 and 5). With respect to DNA sequences orthology analysis is first based on the phylogeny of the sequences, and not with respect to function, allowing the use of either coding or noncoding regions or both. This is usually done manually although attempts have been made to combine alignments with phylogenetic analysis in one step, such as the POY (Phylogeny Reconstruction via Optimization of DNA and other Data) program (W et al. 2003) for which the methods are based on W (1996, 1999). This method does not yet carry out orthologous analysis and, being based upon parsimony, may therefore yield erroneous results. As far as I know, no attempts were made to automate the classifying of multigenes into orthologous sequences. One needs a species tree to rigorously identify orthologous genes, but it is impossible to find a species tree unless the orthologous sequences of the species are known. With one gene we obtain a gene tree, not a species tree. In a multigene family, such as the 5S rdna the situation is more complicated as there are different unit classes of DNA units which are paralogous with respect to each other. Wendel and associates in an excellent review on the utility of nuclear genes for phylogeny reconstruction (S et al. 2004) emphasized the necessity of cloning prior to sequencing, as we recommended for the Triticeae (B et al. 2001) but only when polymorphism is detected at the gene amplification stage by PCR for orthology assessment. They did not take into consideration that sequence polymorphism may occur even when the PCR products appear uniform. They also advocated the use of BLAST as one of the steps in the assessment of putative orthology, as we had done. When PCR amplification reveals two or more types (bands on a gel), then clearly direct sequencing of the unpurified PCR products is not realistic. It is less well recognized that sequence polymorphism may occur even when the PCR products appear uniform in size (B et al. 2001) and that cloning of PCR products remains necessary in this case too. S et al. (2004) also recommended developing locus specific primers once types had been defined. Although we have successfully used such probes for the analysis of different unit classes via FISH (B et al. 2004), we advocate sequencing of many clones in order to establish putative orthology classes and to provide strong support for them. As described above, multiple 5S rdna unit classes are seen in the grasses. Wendel and associates found only one putative orthologous group of sequences per haplome in Gossypium, perhaps because of the nature of this genus, or because of gene loss due to a deletion or a failure to sequence enough samples. Sufficient sampling remains a vexatious problem (B et al. 2001) for which we have no solution. ML analyses Testing orthology and haplome divergence. Whether sequence alignment it is carried by automatic means or by a combination of programs such as BLAST and alignment programs followed by manual refinement, as is conventionally carried out, it is obviously the most important step in determining putative orthology. This is as true for single copy genes as it is for multicopy genes such as the 5S DNA unit classes in the Triticeae. Tests of orthology rely on phylogenetic analysis of the putative orthologs, e.g., unit classes in Triticeae. Parsimony, although useful under certain conditions, lacks an explicit model of evolution (G 1990). In recent years great progress has been made especially in ML algorithms. ML methods allow both a wide variety of phylogenetic inferences from sequence data and robust statistical assessment of all results (W et al. 2001). The authors went so far as to express the opinion that it cannot remain acceptable to use outdated data analysis techniques when superior alternatives exist (ibid) as some have done. In the tests of putative orthology the DNA sequences that belong to the same unit class were mostly, if not all, found on small branches of the tree compared to the much longer branches which subtended the clusters of the orthologs, i.e. the unit classes (Figures 3 and 5 for example). To carry out the relationship among the groups of orthologs we first selected exemplar sequences from each orthologous group (unit class) and then subjected the data to tests of fitting the substitution model from among the different models of evolution so far defined; and then subjected the data to ML analyses with the parameters of the models. An assessment of the robustness of the resulting trees was achieved by non-parametric bootstrapping (F 1985; S et al. 1998) and including Bayesian analysis (H & R 48 Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

11 2001). Using this approach we can estimate the phylogenetic relationships among haplomes in the Triticeae, in other words haplome relationships can be estimated by the phylogenetic relationships among the unit classes and is thus also achieved with strong statistical support. An example of this procedure was described above for the I haplome and H haplome diploid Hordeum species. Total evidence versus supertrees The different unit classes in the Triticeae, i.e. the different groups of orthologs are paralogous groups. They need to be combined for a global analysis. Inferring phylogeny relationships by analysing combined data of different kinds, e.g. morphology and gene sequences, sequences from different genes, DNA-DNA hybridization with DNA sequences or serological data with any of these or any combination, requires comparison of like with like. This is a controversial issue, because gene phylogenies may be incongruent with organismal phylogenies. Some authors like to make separate phylogeny estimates from different data sets, and then test their congruence as in total evidence, i.e. the matrix of evidence is analyzed as one whole without being partitioned (K 1989, 2004). The advantage of using supertrees instead is that these methods, such as Matrix Representation using Parsimony (MRP) (B 1992; R 1992; B & R 2004) retain the information contained in each of the different genes (or paralogs) when combining them. Analysis by supertrees enables analysis of paralogous sets of sequences from a multigene family such as the 5S DNA gene. CONCLUSION While the use of sequence data from multigene families to infer phylogeny is not without challenge, the methods that we have described in several publications and summarized here, provide a sound framework for such analyses. The results to date based mainly upon sequences of the 5S rdna NTS are proving to be useful for inferring possible relationships among haplomes in the Triticeae, and for constructing hypotheses about their evolution. These approaches should be applicable to other multigene families Acknowledgements. I thank Dr. D.A J, University of O awa (UO) and Mr. L.G. B, Agriculture & Agri-Food Canada (AAFC). Without their cooperation in generating sequence data and Dr. J in co-authoring numerous papers this work could have never been achieved. I thank collaborators H -Y S and Y -M W, of the Triticeae Research Institute, Sichuan Agricultural University, China, for the Secale sequences. An earlier version of the manuscript benefited from the comments by Drs. J, UO, E. S and B. M both AAFC. References A S.F., G W., M W., M E.W., L D.J. (1990): Basic local alignment search tool. Journal of Molecular Biology, 215: A R., B B.R. (1992): Evolution of the Nor and 5SDna loci in the Triticeae. In: S P.S., S D.E., D J.J. (eds.): Molecular Systematics of Plants. Chapman & Hall, New York, U.S.A.: A L., B A.-C., L J., S B. (2003): Bioinformatics, 10 (Suppl. 1): i7 i15. B B.R. (1992): Combining trees as a way of combining datasets for phylogenetic inference, and the desirability of combining gene trees. Taxon, 41: B B.R., A R. (1992): Evolutionary change at the 5S DNA loci of species in the Triticeae. Plant Systematics and Evolution, 183: B B.R., B L.G. (1997): The molecular diversity of the 5S rrna gene in Kengyilia alatavica (Drobov) J.L. Yang, Yen & Baum (Poaceae: Triticeae): potential genomic assignment of different rdna units. Genome, 40: B B.R., B L.G. (2000): The 5S rdna units in Kengyilia (Poaceae: Triticeae): diversity of the non-transcribed spacer and genomic relationships. Canadian Journal of Botany, 78: B B.R., B L.G. (2001): The 5S rrna gene sequence variation in wheats and some polyploid wheat progenitors (Poaceae: Triticeae). Genetic Resources and Crop Evolution, 48: B B.R., J D.A. (1994): The molecular diversity of the 5S rrna gene in barley (Hordeum vulgare). Genome, 37: B B.R., J D.A. (1996): The 5S rrna gene units in ancestral two-row barley (Hordeum spontaneum C. Koch) and bulbous barley (H. bulbosum L.): sequence analysis and phylogenetic relationships with the 5S rdna units in cultivated barley (H. vulgare L.). Genome, 39: B B.R., J D.A. (1998): The 5S rrna gene in sea barley (Hordeum marinum Hudson sensu lato): sequence variation among repeat units and relation- Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

12 ship to the X haplome in barley (Hordeum). Genome, 41: B B.R., J D.A. (1999): The 5S rrna gene in wall barley (Hordeum murinum L. sensu lato): sequence variation among repeat units and relationship to the Y haplome in the genus Hordeum (Poaceae: Triticeae). Genome, 42: B B.R., J D.A. (2000): The 5S rrna gene units in the native New World annual Hordeum species (Triticeae: Poaceae). Canadian Journal of Botany, 78: B B.R., J D.A. (2002): A comparison of the 5S rdna diversity in the Hordeum brachyantherumcalifornicum complex with those of the eastern Asiatic Hordeum roshevitzii and the South American Hordeum cordobense (Triticeae: Poaceae). Canadian Journal of Botany, 80: B B.R., J D.A. (2003): The South African Hordeum capense is more closely related to some American Hordeum species than to the European Hordeum secalinum: a perspective based on the 5S DNA units (Triticeae: Poaceae). Canadian Journal of Botany, 81: B B.R., R M.A. (2004): The MRP method. In: B -E O.R.P. (ed.): Phylogenetic Supertrees. Combining information to reveal the tree of life. Kluwer, Dordrecht, the Netherlands: B B.R., B L.G., J D.A., A A.V. (2003): Molecular diversity of the 5S rdna units in the Elymus dahuricus complex (Poaceae: Triticeae) supports the genomic constitution of St, Y, and H haplomes. Canadian Journal of Botany, B B.R., B L.G., B A., R O., N E. (2004): The utility of the nontranscribed spacer of 5S rdna units grouped into unit classes assigned to haplomes a test on cultivated wheat and wheat progenitors. Genome, 47: B B.R., J D.A., B L.G. (2001): Defining groups among multicopy genes prior to inferring phylogeny, with special emphasis on the Triticeae (Poaceae). Hereditas, 135: B B.R., J D.A., B L.G. (2005): Ancient differentiation of the H and I haplomes in diploid Hordeum species based on 5S rdna. Genome, 48: B R., F J., L T. (1986): Meiosis in Interspecific Hordeum hybrids. I. Diploid combinations. Canadian Journal of Genetics and Cytology, 28: B R., F J., L T. (1987): Meiosis in Interspecific Hordeum hybrids. II. Triploid hybrids. Evolutionary Trends in Plants, 1: B R., J N. (1991): Interspecific hybrids within the genus Hordeum. In: G P.K., T T. (eds.): Chromosome Engineering in Plants: Genetics, Breeding, Evolution, Part A. Elsevier, Amsterdam, the Netherlands: E J.A. (1998): Phylogenomics: improving functional predictions for uncharacterized genes by evolutionary analysis. Genome Research, 8: F J. (1985): Confidence limits on phylogenies: an approach using the bootstrap. Evolution, 39: F J. (2004): Inferring phylogenies. Sinauer Associates, Sunderland. G N. (1990): Maximum likelihood inference of phylogenetic trees, with special reference to a Poisson process model of DNA substitution and to parsimony analysis. Systematic Zoology, 39: H M., K K., Y T. (1985): Dating the human-ape spli ing by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution, 22: H J.P., R F. (2001): MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics, 17: K A.G. (1989): A concern for evidence and a phylogenetic hypothesis of relationships among Epicrates (Boidae, Serpentes). Systematic Zoology, 38: K A.G. (2004): On total evidence: for the record. Cladistics, 20: O G.J., M H., H R., O R. (1994): FastDNAml: A tool for construction of phylogenetic trees of DNA sequences using maximum likelihood. Computer Applications in the Biosciences, 10: P G., S O. (2004): On the origin of the tetraploid species Hordeum capense and H. secalinum (Poaceae). Systematic Botany, 29: P D. (2003): Selecting models of evolution. Theory. In: S M.; V A.-M. (eds.): The Phylogenetic Handbook. A practical Approach to DNA and Protein Phylogeny. Cambridge University Press, Cambridge, U.K. P D., C K.A. (1998): Modeltest: Testing the model of DNA substitution. Bioinformatics, 14: R M.A. (1992): Phylogenetic inference based on matrix representation of trees. Molecular Phylogenetics and Evolution, 1: S N., N M. (1987): The neighbour-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4: Proc. 5 th International Triticeae Symposium, Prague, June 6 10, 2005

13 S R.L., C R.C., W J.F. (2004): Use of nuclear genes for phylogeny reconstruction in plants. Australian Journal of Botany, 17: S D.L. (1998): PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods). Version 4. Sinauer Associates, Sunderland. Massachuse s. T R.L., N D.A., G I.V., T T.A., S U.T., R B.S., K B., G - M.Y., F N.D., K E.V. (2001): The COG database: new developments in phylogenetic classification of protein from complete genomes. Nucleic Acid Research, 29: W S., L, G N. (2001): Molecular phylogenetics: state-of-the-art methods for looking into the past. Trends in Genetics, 17: W W. (1996): Optimization alignment: the end of multiple sequence alignment in phylogenetics? Cladistics, 12: 1 9. W W.C. (1999): Fixed character States and the optimization of molecular sequence data. Cladistics, 15: W W., G D., D L J. (2003): POY version available at h p://research.amnh.org/ scicomp/projects/poy.php. Y Z. (1994): Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. Journal of Molecular Evolution, 39: Y -P C., V J., M J. (1985): Improved M13 phage cloning vectors and host strains: nucleotide sequence of M13mp18 and puc19 vectors. Gene, 33: Z C.M., E S.R. (2002): RIO: Analyzing proteomes by automated phylogenomics using resampled inference orthologs. BMC Bioinformatics, 3: Proc. 5 th International Triticeae Symposium, Prague, June 6 10,

Introduction to Bioinformatics 3. DNA editing and contig assembly

Introduction to Bioinformatics 3. DNA editing and contig assembly Introduction to Bioinformatics 3. DNA editing and contig assembly Benjamin F. Matthews United States Department of Agriculture Soybean Genomics and Improvement Laboratory Beltsville, MD 20708 matthewb@ba.ars.usda.gov

More information

Phylogenetic Trees Made Easy

Phylogenetic Trees Made Easy Phylogenetic Trees Made Easy A How-To Manual Fourth Edition Barry G. Hall University of Rochester, Emeritus and Bellingham Research Institute Sinauer Associates, Inc. Publishers Sunderland, Massachusetts

More information

Lab 2/Phylogenetics/September 16, 2002 1 PHYLOGENETICS

Lab 2/Phylogenetics/September 16, 2002 1 PHYLOGENETICS Lab 2/Phylogenetics/September 16, 2002 1 Read: Tudge Chapter 2 PHYLOGENETICS Objective of the Lab: To understand how DNA and protein sequence information can be used to make comparisons and assess evolutionary

More information

The Central Dogma of Molecular Biology

The Central Dogma of Molecular Biology Vierstraete Andy (version 1.01) 1/02/2000 -Page 1 - The Central Dogma of Molecular Biology Figure 1 : The Central Dogma of molecular biology. DNA contains the complete genetic information that defines

More information

Introduction to Phylogenetic Analysis

Introduction to Phylogenetic Analysis Subjects of this lecture Introduction to Phylogenetic nalysis Irit Orr 1 Introducing some of the terminology of phylogenetics. 2 Introducing some of the most commonly used methods for phylogenetic analysis.

More information

A Primer of Genome Science THIRD

A Primer of Genome Science THIRD A Primer of Genome Science THIRD EDITION GREG GIBSON-SPENCER V. MUSE North Carolina State University Sinauer Associates, Inc. Publishers Sunderland, Massachusetts USA Contents Preface xi 1 Genome Projects:

More information

Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company

Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Chapter 8: Recombinant DNA 2002 by W. H. Freeman and Company Genetic engineering: humans Gene replacement therapy or gene therapy Many technical and ethical issues implications for gene pool for germ-line gene therapy what traits constitute disease rather than just

More information

Protocols. Internal transcribed spacer region (ITS) region. Niklaus J. Grünwald, Frank N. Martin, and Meg M. Larsen (2013)

Protocols. Internal transcribed spacer region (ITS) region. Niklaus J. Grünwald, Frank N. Martin, and Meg M. Larsen (2013) Protocols Internal transcribed spacer region (ITS) region Niklaus J. Grünwald, Frank N. Martin, and Meg M. Larsen (2013) The nuclear ribosomal RNA (rrna) genes (small subunit, large subunit and 5.8S) are

More information

Bayesian Phylogeny and Measures of Branch Support

Bayesian Phylogeny and Measures of Branch Support Bayesian Phylogeny and Measures of Branch Support Bayesian Statistics Imagine we have a bag containing 100 dice of which we know that 90 are fair and 10 are biased. The

More information

Supporting Online Material for

Supporting Online Material for www.sciencemag.org/cgi/content/full/312/5781/1762/dc1 Supporting Online Material for Silk Genes Support the Single Origin of Orb Webs Jessica E. Garb,* Teresa DiMauro, Victoria Vo, Cheryl Y. Hayashi *To

More information

CCR Biology - Chapter 9 Practice Test - Summer 2012

CCR Biology - Chapter 9 Practice Test - Summer 2012 Name: Class: Date: CCR Biology - Chapter 9 Practice Test - Summer 2012 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Genetic engineering is possible

More information

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources

Just the Facts: A Basic Introduction to the Science Underlying NCBI Resources 1 of 8 11/7/2004 11:00 AM National Center for Biotechnology Information About NCBI NCBI at a Glance A Science Primer Human Genome Resources Model Organisms Guide Outreach and Education Databases and Tools

More information

Sequence Analysis 15: lecture 5. Substitution matrices Multiple sequence alignment

Sequence Analysis 15: lecture 5. Substitution matrices Multiple sequence alignment Sequence Analysis 15: lecture 5 Substitution matrices Multiple sequence alignment A teacher's dilemma To understand... Multiple sequence alignment Substitution matrices Phylogenetic trees You first need

More information

Protein Sequence Analysis - Overview -

Protein Sequence Analysis - Overview - Protein Sequence Analysis - Overview - UDEL Workshop Raja Mazumder Research Associate Professor, Department of Biochemistry and Molecular Biology Georgetown University Medical Center Topics Why do protein

More information

A short guide to phylogeny reconstruction

A short guide to phylogeny reconstruction A short guide to phylogeny reconstruction E. Michu Institute of Biophysics, Academy of Sciences of the Czech Republic, Brno, Czech Republic ABSTRACT This review is a short introduction to phylogenetic

More information

GENEWIZ, Inc. DNA Sequencing Service Details for USC Norris Comprehensive Cancer Center DNA Core

GENEWIZ, Inc. DNA Sequencing Service Details for USC Norris Comprehensive Cancer Center DNA Core DNA Sequencing Services Pre-Mixed o Provide template and primer, mixed into the same tube* Pre-Defined o Provide template and primer in separate tubes* Custom o Full-service for samples with unknown concentration

More information

Introduction to Bioinformatics AS 250.265 Laboratory Assignment 6

Introduction to Bioinformatics AS 250.265 Laboratory Assignment 6 Introduction to Bioinformatics AS 250.265 Laboratory Assignment 6 In the last lab, you learned how to perform basic multiple sequence alignments. While useful in themselves for determining conserved residues

More information

PHYLOGENY AND EVOLUTION OF NEWCASTLE DISEASE VIRUS GENOTYPES

PHYLOGENY AND EVOLUTION OF NEWCASTLE DISEASE VIRUS GENOTYPES Eötvös Lóránd University Biology Doctorate School Classical and molecular genetics program Project leader: Dr. László Orosz, corresponding member of HAS PHYLOGENY AND EVOLUTION OF NEWCASTLE DISEASE VIRUS

More information

GenBank, Entrez, & FASTA

GenBank, Entrez, & FASTA GenBank, Entrez, & FASTA Nucleotide Sequence Databases First generation GenBank is a representative example started as sort of a museum to preserve knowledge of a sequence from first discovery great repositories,

More information

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison

RETRIEVING SEQUENCE INFORMATION. Nucleotide sequence databases. Database search. Sequence alignment and comparison RETRIEVING SEQUENCE INFORMATION Nucleotide sequence databases Database search Sequence alignment and comparison Biological sequence databases Originally just a storage place for sequences. Currently the

More information

HIGH DENSITY DATA STORAGE IN DNA USING AN EFFICIENT MESSAGE ENCODING SCHEME Rahul Vishwakarma 1 and Newsha Amiri 2

HIGH DENSITY DATA STORAGE IN DNA USING AN EFFICIENT MESSAGE ENCODING SCHEME Rahul Vishwakarma 1 and Newsha Amiri 2 HIGH DENSITY DATA STORAGE IN DNA USING AN EFFICIENT MESSAGE ENCODING SCHEME Rahul Vishwakarma 1 and Newsha Amiri 2 1 Tata Consultancy Services, India derahul@ieee.org 2 Bangalore University, India ABSTRACT

More information

Molecular Clocks and Tree Dating with r8s and BEAST

Molecular Clocks and Tree Dating with r8s and BEAST Integrative Biology 200B University of California, Berkeley Principals of Phylogenetics: Ecology and Evolution Spring 2011 Updated by Nick Matzke Molecular Clocks and Tree Dating with r8s and BEAST Today

More information

DNA Sample preparation and Submission Guidelines

DNA Sample preparation and Submission Guidelines DNA Sample preparation and Submission Guidelines Requirements: Please submit samples in 1.5ml microcentrifuge tubes. Fill all the required information in the Eurofins DNA sequencing order form and send

More information

A data management framework for the Fungal Tree of Life

A data management framework for the Fungal Tree of Life Web Accessible Sequence Analysis for Biological Inference A data management framework for the Fungal Tree of Life Kauff F, Cox CJ, Lutzoni F. 2007. WASABI: An automated sequence processing system for multi-gene

More information

A Step-by-Step Tutorial: Divergence Time Estimation with Approximate Likelihood Calculation Using MCMCTREE in PAML

A Step-by-Step Tutorial: Divergence Time Estimation with Approximate Likelihood Calculation Using MCMCTREE in PAML 9 June 2011 A Step-by-Step Tutorial: Divergence Time Estimation with Approximate Likelihood Calculation Using MCMCTREE in PAML by Jun Inoue, Mario dos Reis, and Ziheng Yang In this tutorial we will analyze

More information

Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.

Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question. Name: Class: Date: Chapter 17 Practice Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The correct order for the levels of Linnaeus's classification system,

More information

Core Bioinformatics. Degree Type Year Semester. 4313473 Bioinformàtica/Bioinformatics OB 0 1

Core Bioinformatics. Degree Type Year Semester. 4313473 Bioinformàtica/Bioinformatics OB 0 1 Core Bioinformatics 2014/2015 Code: 42397 ECTS Credits: 12 Degree Type Year Semester 4313473 Bioinformàtica/Bioinformatics OB 0 1 Contact Name: Sònia Casillas Viladerrams Email: Sonia.Casillas@uab.cat

More information

MATCH Commun. Math. Comput. Chem. 61 (2009) 781-788

MATCH Commun. Math. Comput. Chem. 61 (2009) 781-788 MATCH Communications in Mathematical and in Computer Chemistry MATCH Commun. Math. Comput. Chem. 61 (2009) 781-788 ISSN 0340-6253 Three distances for rapid similarity analysis of DNA sequences Wei Chen,

More information

Algorithms in Computational Biology (236522) spring 2007 Lecture #1

Algorithms in Computational Biology (236522) spring 2007 Lecture #1 Algorithms in Computational Biology (236522) spring 2007 Lecture #1 Lecturer: Shlomo Moran, Taub 639, tel 4363 Office hours: Tuesday 11:00-12:00/by appointment TA: Ilan Gronau, Taub 700, tel 4894 Office

More information

Molecular typing of VTEC: from PFGE to NGS-based phylogeny

Molecular typing of VTEC: from PFGE to NGS-based phylogeny Molecular typing of VTEC: from PFGE to NGS-based phylogeny Valeria Michelacci 10th Annual Workshop of the National Reference Laboratories for E. coli in the EU Rome, November 5 th 2015 Molecular typing

More information

Current Motif Discovery Tools and their Limitations

Current Motif Discovery Tools and their Limitations Current Motif Discovery Tools and their Limitations Philipp Bucher SIB / CIG Workshop 3 October 2006 Trendy Concepts and Hypotheses Transcription regulatory elements act in a context-dependent manner.

More information

Nucleic Acid Techniques in Bacterial Systematics

Nucleic Acid Techniques in Bacterial Systematics Nucleic Acid Techniques in Bacterial Systematics Edited by Erko Stackebrandt Department of Microbiology University of Queensland St Lucia, Australia and Michael Goodfellow Department of Microbiology University

More information

INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE Q5B

INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE Q5B INTERNATIONAL CONFERENCE ON HARMONISATION OF TECHNICAL REQUIREMENTS FOR REGISTRATION OF PHARMACEUTICALS FOR HUMAN USE ICH HARMONISED TRIPARTITE GUIDELINE QUALITY OF BIOTECHNOLOGICAL PRODUCTS: ANALYSIS

More information

Forensic DNA Testing Terminology

Forensic DNA Testing Terminology Forensic DNA Testing Terminology ABI 310 Genetic Analyzer a capillary electrophoresis instrument used by forensic DNA laboratories to separate short tandem repeat (STR) loci on the basis of their size.

More information

Name Class Date. binomial nomenclature. MAIN IDEA: Linnaeus developed the scientific naming system still used today.

Name Class Date. binomial nomenclature. MAIN IDEA: Linnaeus developed the scientific naming system still used today. Section 1: The Linnaean System of Classification 17.1 Reading Guide KEY CONCEPT Organisms can be classified based on physical similarities. VOCABULARY taxonomy taxon binomial nomenclature genus MAIN IDEA:

More information

Bioinformatics Resources at a Glance

Bioinformatics Resources at a Glance Bioinformatics Resources at a Glance A Note about FASTA Format There are MANY free bioinformatics tools available online. Bioinformaticists have developed a standard format for nucleotide and protein sequences

More information

Innovations in Molecular Epidemiology

Innovations in Molecular Epidemiology Innovations in Molecular Epidemiology Molecular Epidemiology Measure current rates of active transmission Determine whether recurrent tuberculosis is attributable to exogenous reinfection Determine whether

More information

Missing data and the accuracy of Bayesian phylogenetics

Missing data and the accuracy of Bayesian phylogenetics Journal of Systematics and Evolution 46 (3): 307 314 (2008) (formerly Acta Phytotaxonomica Sinica) doi: 10.3724/SP.J.1002.2008.08040 http://www.plantsystematics.com Missing data and the accuracy of Bayesian

More information

Bio-Informatics Lectures. A Short Introduction

Bio-Informatics Lectures. A Short Introduction Bio-Informatics Lectures A Short Introduction The History of Bioinformatics Sanger Sequencing PCR in presence of fluorescent, chain-terminating dideoxynucleotides Massively Parallel Sequencing Massively

More information

A comparison of methods for estimating the transition:transversion ratio from DNA sequences

A comparison of methods for estimating the transition:transversion ratio from DNA sequences Molecular Phylogenetics and Evolution 32 (2004) 495 503 MOLECULAR PHYLOGENETICS AND EVOLUTION www.elsevier.com/locate/ympev A comparison of methods for estimating the transition:transversion ratio from

More information

European Medicines Agency

European Medicines Agency European Medicines Agency July 1996 CPMP/ICH/139/95 ICH Topic Q 5 B Quality of Biotechnological Products: Analysis of the Expression Construct in Cell Lines Used for Production of r-dna Derived Protein

More information

LabGenius. Technical design notes. The world s most advanced synthetic DNA libraries. hi@labgeni.us V1.5 NOV 15

LabGenius. Technical design notes. The world s most advanced synthetic DNA libraries. hi@labgeni.us V1.5 NOV 15 LabGenius The world s most advanced synthetic DNA libraries Technical design notes hi@labgeni.us V1.5 NOV 15 Introduction OUR APPROACH LabGenius is a gene synthesis company focussed on the design and manufacture

More information

DNA Insertions and Deletions in the Human Genome. Philipp W. Messer

DNA Insertions and Deletions in the Human Genome. Philipp W. Messer DNA Insertions and Deletions in the Human Genome Philipp W. Messer Genetic Variation CGACAATAGCGCTCTTACTACGTGTATCG : : CGACAATGGCGCT---ACTACGTGCATCG 1. Nucleotide mutations 2. Genomic rearrangements 3.

More information

Final Project Report

Final Project Report CPSC545 by Introduction to Data Mining Prof. Martin Schultz & Prof. Mark Gerstein Student Name: Yu Kor Hugo Lam Student ID : 904907866 Due Date : May 7, 2007 Introduction Final Project Report Pseudogenes

More information

Genome Explorer For Comparative Genome Analysis

Genome Explorer For Comparative Genome Analysis Genome Explorer For Comparative Genome Analysis Jenn Conn 1, Jo L. Dicks 1 and Ian N. Roberts 2 Abstract Genome Explorer brings together the tools required to build and compare phylogenies from both sequence

More information

Extensive Cryptic Diversity in Indo-Australian Rainbowfishes Revealed by DNA Barcoding

Extensive Cryptic Diversity in Indo-Australian Rainbowfishes Revealed by DNA Barcoding Extensive Cryptic Diversity in Indo-Australian Rainbowfishes Revealed by DNA Barcoding Kadarusman, Hubert N, Hadiaty R.K #, Sudarto, Paradis E., Pouyaud L. Akademi Perikanan Sorong, Papua Barat, Indonesia

More information

Genetic Analysis. Phenotype analysis: biological-biochemical analysis. Genotype analysis: molecular and physical analysis

Genetic Analysis. Phenotype analysis: biological-biochemical analysis. Genotype analysis: molecular and physical analysis Genetic Analysis Phenotype analysis: biological-biochemical analysis Behaviour under specific environmental conditions Behaviour of specific genetic configurations Behaviour of progeny in crosses - Genotype

More information

Data for phylogenetic analysis

Data for phylogenetic analysis Data for phylogenetic analysis The data that are used to estimate the phylogeny of a set of tips are the characteristics of those tips. Therefore the success of phylogenetic inference depends in large

More information

Data Partitions and Complex Models in Bayesian Analysis: The Phylogeny of Gymnophthalmid Lizards

Data Partitions and Complex Models in Bayesian Analysis: The Phylogeny of Gymnophthalmid Lizards Syst. Biol. 53(3):448 469, 2004 Copyright c Society of Systematic Biologists ISSN: 1063-5157 print / 1076-836X online DOI: 10.1080/10635150490445797 Data Partitions and Complex Models in Bayesian Analysis:

More information

DNA Barcoding in Plants: Biodiversity Identification and Discovery

DNA Barcoding in Plants: Biodiversity Identification and Discovery DNA Barcoding in Plants: Biodiversity Identification and Discovery University of Sao Paulo December 2009 W. John Kress Department of Botany National Museum of Natural History Smithsonian Institution New

More information

A branch-and-bound algorithm for the inference of ancestral. amino-acid sequences when the replacement rate varies among

A branch-and-bound algorithm for the inference of ancestral. amino-acid sequences when the replacement rate varies among A branch-and-bound algorithm for the inference of ancestral amino-acid sequences when the replacement rate varies among sites Tal Pupko 1,*, Itsik Pe er 2, Masami Hasegawa 1, Dan Graur 3, and Nir Friedman

More information

ADVANCES IN BOTANICAL RESEARCH

ADVANCES IN BOTANICAL RESEARCH o >VOLUME SIXTY NINE ADVANCES IN BOTANICAL RESEARCH Genomes of Herbaceous Land Plants Volume Editor ANDREW H. PATERSON Plant Genome Mapping Laboratory Department of Crop and Soil Sciences, Department of

More information

Multiple Losses of Flight and Recent Speciation in Steamer Ducks Tara L. Fulton, Brandon Letts, and Beth Shapiro

Multiple Losses of Flight and Recent Speciation in Steamer Ducks Tara L. Fulton, Brandon Letts, and Beth Shapiro Supplementary Material for: Multiple Losses of Flight and Recent Speciation in Steamer Ducks Tara L. Fulton, Brandon Letts, and Beth Shapiro 1. Supplementary Tables Supplementary Table S1. Sample information.

More information

Basic Concepts of DNA, Proteins, Genes and Genomes

Basic Concepts of DNA, Proteins, Genes and Genomes Basic Concepts of DNA, Proteins, Genes and Genomes Kun-Mao Chao 1,2,3 1 Graduate Institute of Biomedical Electronics and Bioinformatics 2 Department of Computer Science and Information Engineering 3 Graduate

More information

Becker Muscular Dystrophy

Becker Muscular Dystrophy Muscular Dystrophy A Case Study of Positional Cloning Described by Benjamin Duchenne (1868) X-linked recessive disease causing severe muscular degeneration. 100 % penetrance X d Y affected male Frequency

More information

MORPHEUS. http://biodev.cea.fr/morpheus/ Prediction of Transcription Factors Binding Sites based on Position Weight Matrix.

MORPHEUS. http://biodev.cea.fr/morpheus/ Prediction of Transcription Factors Binding Sites based on Position Weight Matrix. MORPHEUS http://biodev.cea.fr/morpheus/ Prediction of Transcription Factors Binding Sites based on Position Weight Matrix. Reference: MORPHEUS, a Webtool for Transcripton Factor Binding Analysis Using

More information

Human Genome and Human Genome Project. Louxin Zhang

Human Genome and Human Genome Project. Louxin Zhang Human Genome and Human Genome Project Louxin Zhang A Primer to Genomics Cells are the fundamental working units of every living systems. DNA is made of 4 nucleotide bases. The DNA sequence is the particular

More information

Annex 6: Nucleotide Sequence Information System BEETLE. Biological and Ecological Evaluation towards Long-Term Effects

Annex 6: Nucleotide Sequence Information System BEETLE. Biological and Ecological Evaluation towards Long-Term Effects Annex 6: Nucleotide Sequence Information System BEETLE Biological and Ecological Evaluation towards Long-Term Effects Long-term effects of genetically modified (GM) crops on health, biodiversity and the

More information

Molecular and Cell Biology Laboratory (BIOL-UA 223) Instructor: Ignatius Tan Phone: 212-998-8295 Office: 764 Brown Email: ignatius.tan@nyu.

Molecular and Cell Biology Laboratory (BIOL-UA 223) Instructor: Ignatius Tan Phone: 212-998-8295 Office: 764 Brown Email: ignatius.tan@nyu. Molecular and Cell Biology Laboratory (BIOL-UA 223) Instructor: Ignatius Tan Phone: 212-998-8295 Office: 764 Brown Email: ignatius.tan@nyu.edu Course Hours: Section 1: Mon: 12:30-3:15 Section 2: Wed: 12:30-3:15

More information

BactoGeNIE: a large-scale comparative genome visualization for big displays

BactoGeNIE: a large-scale comparative genome visualization for big displays RESEARCH Open Access BactoGeNIE: a large-scale comparative genome visualization for big displays Jillian Aurisano 1*, Khairi Reda 2,3, Andrew Johnson 1, Elisabeta G Marai 1, Jason Leigh 3 From 5th Symposium

More information

PROC. CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE 2006 1. E-mail: msm_eng@k-space.org

PROC. CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE 2006 1. E-mail: msm_eng@k-space.org BIOINFTool: Bioinformatics and sequence data analysis in molecular biology using Matlab Mai S. Mabrouk 1, Marwa Hamdy 2, Marwa Mamdouh 2, Marwa Aboelfotoh 2,Yasser M. Kadah 2 1 Biomedical Engineering Department,

More information

The Human Genome Project

The Human Genome Project The Human Genome Project Brief History of the Human Genome Project Physical Chromosome Maps Genetic (or Linkage) Maps DNA Markers Sequencing and Annotating Genomic DNA What Have We learned from the HGP?

More information

Rules and Format for Taxonomic Nucleotide Sequence Annotation for Fungi: a proposal

Rules and Format for Taxonomic Nucleotide Sequence Annotation for Fungi: a proposal Rules and Format for Taxonomic Nucleotide Sequence Annotation for Fungi: a proposal The need for third-party sequence annotation Taxonomic names attached to nucleotide sequences occasionally need to be

More information

restriction enzymes 350 Home R. Ward: Spring 2001

restriction enzymes 350 Home R. Ward: Spring 2001 restriction enzymes 350 Home Restriction Enzymes (endonucleases): molecular scissors that cut DNA Properties of widely used Type II restriction enzymes: recognize a single sequence of bases in dsdna, usually

More information

Biological Sciences Initiative. Human Genome

Biological Sciences Initiative. Human Genome Biological Sciences Initiative HHMI Human Genome Introduction In 2000, researchers from around the world published a draft sequence of the entire genome. 20 labs from 6 countries worked on the sequence.

More information

Protein Protein Interaction Networks

Protein Protein Interaction Networks Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks Young-Rae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics

More information

HCS604.03 Exercise 1 Dr. Jones Spring 2005. Recombinant DNA (Molecular Cloning) exercise:

HCS604.03 Exercise 1 Dr. Jones Spring 2005. Recombinant DNA (Molecular Cloning) exercise: HCS604.03 Exercise 1 Dr. Jones Spring 2005 Recombinant DNA (Molecular Cloning) exercise: The purpose of this exercise is to learn techniques used to create recombinant DNA or clone genes. You will clone

More information

Biotechnology: DNA Technology & Genomics

Biotechnology: DNA Technology & Genomics Chapter 20. Biotechnology: DNA Technology & Genomics 2003-2004 The BIG Questions How can we use our knowledge of DNA to: diagnose disease or defect? cure disease or defect? change/improve organisms? What

More information

DnaSP, DNA polymorphism analyses by the coalescent and other methods.

DnaSP, DNA polymorphism analyses by the coalescent and other methods. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Author affiliation: Julio Rozas 1, *, Juan C. Sánchez-DelBarrio 2,3, Xavier Messeguer 2 and Ricardo Rozas 1 1 Departament de Genètica,

More information

Mitochondrial DNA Analysis

Mitochondrial DNA Analysis Mitochondrial DNA Analysis Lineage Markers Lineage markers are passed down from generation to generation without changing Except for rare mutation events They can help determine the lineage (family tree)

More information

Y Chromosome Markers

Y Chromosome Markers Y Chromosome Markers Lineage Markers Autosomal chromosomes recombine with each meiosis Y and Mitochondrial DNA does not This means that the Y and mtdna remains constant from generation to generation Except

More information

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING

SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING AAS 07-228 SPECIAL PERTURBATIONS UNCORRELATED TRACK PROCESSING INTRODUCTION James G. Miller * Two historical uncorrelated track (UCT) processing approaches have been employed using general perturbations

More information

Chapter 12. GARBAGE IN, GARBAGE OUT Data issues in supertree construction. 1. Introduction

Chapter 12. GARBAGE IN, GARBAGE OUT Data issues in supertree construction. 1. Introduction Chapter 12 GARBAGE IN, GARBAGE OUT Data issues in supertree construction Olaf R. P. Bininda-Emonds, Kate E. Jones, Samantha A. Price, Marcel Cardillo, Richard Grenyer, and Andy Purvis Abstract: Keywords:

More information

Systematic discovery of regulatory motifs in human promoters and 30 UTRs by comparison of several mammals

Systematic discovery of regulatory motifs in human promoters and 30 UTRs by comparison of several mammals Systematic discovery of regulatory motifs in human promoters and 30 UTRs by comparison of several mammals Xiaohui Xie 1, Jun Lu 1, E. J. Kulbokas 1, Todd R. Golub 1, Vamsi Mootha 1, Kerstin Lindblad-Toh

More information

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0

More information

Introduction to transcriptome analysis using High Throughput Sequencing technologies (HTS)

Introduction to transcriptome analysis using High Throughput Sequencing technologies (HTS) Introduction to transcriptome analysis using High Throughput Sequencing technologies (HTS) A typical RNA Seq experiment Library construction Protocol variations Fragmentation methods RNA: nebulization,

More information

How many of you have checked out the web site on protein-dna interactions?

How many of you have checked out the web site on protein-dna interactions? How many of you have checked out the web site on protein-dna interactions? Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail. Find and be ready to discuss

More information

Data Integration. Lectures 16 & 17. ECS289A, WQ03, Filkov

Data Integration. Lectures 16 & 17. ECS289A, WQ03, Filkov Data Integration Lectures 16 & 17 Lectures Outline Goals for Data Integration Homogeneous data integration time series data (Filkov et al. 2002) Heterogeneous data integration microarray + sequence microarray

More information

July 7th 2009 DNA sequencing

July 7th 2009 DNA sequencing July 7th 2009 DNA sequencing Overview Sequencing technologies Sequencing strategies Sample preparation Sequencing instruments at MPI EVA 2 x 5 x ABI 3730/3730xl 454 FLX Titanium Illumina Genome Analyzer

More information

DNA Replication & Protein Synthesis. This isn t a baaaaaaaddd chapter!!!

DNA Replication & Protein Synthesis. This isn t a baaaaaaaddd chapter!!! DNA Replication & Protein Synthesis This isn t a baaaaaaaddd chapter!!! The Discovery of DNA s Structure Watson and Crick s discovery of DNA s structure was based on almost fifty years of research by other

More information

An example of bioinformatics application on plant breeding projects in Rijk Zwaan

An example of bioinformatics application on plant breeding projects in Rijk Zwaan An example of bioinformatics application on plant breeding projects in Rijk Zwaan Xiangyu Rao 17-08-2012 Introduction of RZ Rijk Zwaan is active worldwide as a vegetable breeding company that focuses on

More information

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions

A greedy algorithm for the DNA sequencing by hybridization with positive and negative errors and information about repetitions BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES, Vol. 59, No. 1, 2011 DOI: 10.2478/v10175-011-0015-0 Varia A greedy algorithm for the DNA sequencing by hybridization with positive and negative

More information

Rapid Acquisition of Unknown DNA Sequence Adjacent to a Known Segment by Multiplex Restriction Site PCR

Rapid Acquisition of Unknown DNA Sequence Adjacent to a Known Segment by Multiplex Restriction Site PCR Rapid Acquisition of Unknown DNA Sequence Adjacent to a Known Segment by Multiplex Restriction Site PCR BioTechniques 25:415-419 (September 1998) ABSTRACT The determination of unknown DNA sequences around

More information

Maximum-Likelihood Estimation of Phylogeny from DNA Sequences When Substitution Rates Differ over Sites1

Maximum-Likelihood Estimation of Phylogeny from DNA Sequences When Substitution Rates Differ over Sites1 Maximum-Likelihood Estimation of Phylogeny from DNA Sequences When Substitution Rates Differ over Sites1 Ziheng Yang Department of Animal Science, Beijing Agricultural University Felsenstein s maximum-likelihood

More information

Lecture 13: DNA Technology. DNA Sequencing. DNA Sequencing Genetic Markers - RFLPs polymerase chain reaction (PCR) products of biotechnology

Lecture 13: DNA Technology. DNA Sequencing. DNA Sequencing Genetic Markers - RFLPs polymerase chain reaction (PCR) products of biotechnology Lecture 13: DNA Technology DNA Sequencing Genetic Markers - RFLPs polymerase chain reaction (PCR) products of biotechnology DNA Sequencing determine order of nucleotides in a strand of DNA > bases = A,

More information

Next Generation Sequencing Technologies in Microbial Ecology. Frank Oliver Glöckner

Next Generation Sequencing Technologies in Microbial Ecology. Frank Oliver Glöckner Next Generation Sequencing Technologies in Microbial Ecology Frank Oliver Glöckner 1 Max Planck Institute for Marine Microbiology Investigation of the role, diversity and features of microorganisms Interactions

More information

BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS

BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS BASIC STATISTICAL METHODS FOR GENOMIC DATA ANALYSIS SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-110 012 seema@iasri.res.in Genomics A genome is an organism s

More information

DNA FINGERPRINTING AND PHYLOGENETIC ANALYSIS OF BACTERIA. DNA fingerprinting and the bacterial 16S-23S rrna intergene region.

DNA FINGERPRINTING AND PHYLOGENETIC ANALYSIS OF BACTERIA. DNA fingerprinting and the bacterial 16S-23S rrna intergene region. MCB4403L SUPPLEMENTAL EXERCISE #3: DNA FINGERPRINTING AND PHYLOGENETIC ANALYSIS OF BACTERIA INTRODUCTION DNA fingerprinting and the bacterial 16S-23S rrna intergene region. Relationships among bacteria

More information

Genetomic Promototypes

Genetomic Promototypes Genetomic Promototypes Mirkó Palla and Dana Pe er Department of Mechanical Engineering Clarkson University Potsdam, New York and Department of Genetics Harvard Medical School 77 Avenue Louis Pasteur Boston,

More information

Name Class Date. Figure 13 1. 2. Which nucleotide in Figure 13 1 indicates the nucleic acid above is RNA? a. uracil c. cytosine b. guanine d.

Name Class Date. Figure 13 1. 2. Which nucleotide in Figure 13 1 indicates the nucleic acid above is RNA? a. uracil c. cytosine b. guanine d. 13 Multiple Choice RNA and Protein Synthesis Chapter Test A Write the letter that best answers the question or completes the statement on the line provided. 1. Which of the following are found in both

More information

Genetic Technology. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.

Genetic Technology. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question. Name: Class: Date: Genetic Technology Multiple Choice Identify the choice that best completes the statement or answers the question. 1. An application of using DNA technology to help environmental scientists

More information

Typing in the NGS era: The way forward!

Typing in the NGS era: The way forward! Typing in the NGS era: The way forward! Valeria Michelacci NGS course, June 2015 Typing from sequence data NGS-derived conventional Multi Locus Sequence Typing (University of Warwick, 7 housekeeping genes)

More information

BIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525-w16

BIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525-w16 Course Director: Dr. Barry Grant (DCM&B, bjgrant@med.umich.edu) Description: This is a three module course covering (1) Foundations of Bioinformatics, (2) Statistics in Bioinformatics, and (3) Systems

More information

DNA Sequence Alignment Analysis

DNA Sequence Alignment Analysis Analysis of DNA sequence data p. 1 Analysis of DNA sequence data using MEGA and DNAsp. Analysis of two genes from the X and Y chromosomes of plant species from the genus Silene The first two computer classes

More information

Microarray Data Analysis Workshop. Custom arrays and Probe design Probe design in a pangenomic world. Carsten Friis. MedVetNet Workshop, DTU 2008

Microarray Data Analysis Workshop. Custom arrays and Probe design Probe design in a pangenomic world. Carsten Friis. MedVetNet Workshop, DTU 2008 Microarray Data Analysis Workshop MedVetNet Workshop, DTU 2008 Custom arrays and Probe design Probe design in a pangenomic world Carsten Friis Media glna tnra GlnA TnrA C2 glnr C3 C5 C6 K GlnR C1 C4 C7

More information

Graph theoretic approach to analyze amino acid network

Graph theoretic approach to analyze amino acid network Int. J. Adv. Appl. Math. and Mech. 2(3) (2015) 31-37 (ISSN: 2347-2529) Journal homepage: www.ijaamm.com International Journal of Advances in Applied Mathematics and Mechanics Graph theoretic approach to

More information

Bioinformatics Grid - Enabled Tools For Biologists.

Bioinformatics Grid - Enabled Tools For Biologists. Bioinformatics Grid - Enabled Tools For Biologists. What is Grid-Enabled Tools (GET)? As number of data from the genomics and proteomics experiment increases. Problems arise for the current sequence analysis

More information

PHYML Online: A Web Server for Fast Maximum Likelihood-Based Phylogenetic Inference

PHYML Online: A Web Server for Fast Maximum Likelihood-Based Phylogenetic Inference PHYML Online: A Web Server for Fast Maximum Likelihood-Based Phylogenetic Inference Stephane Guindon, F. Le Thiec, Patrice Duroux, Olivier Gascuel To cite this version: Stephane Guindon, F. Le Thiec, Patrice

More information

Worksheet - COMPARATIVE MAPPING 1

Worksheet - COMPARATIVE MAPPING 1 Worksheet - COMPARATIVE MAPPING 1 The arrangement of genes and other DNA markers is compared between species in Comparative genome mapping. As early as 1915, the geneticist J.B.S Haldane reported that

More information

(Anisoptera: Libellulidae)

(Anisoptera: Libellulidae) Odonatohgica34(2): 173178 June I, 2005 The morphological forms of Palpopleuralucia (Drury) are separatespecies as evidenced by DNA sequencing (Anisoptera: Libellulidae) A. Mitchell¹ and M.J. Samways ²

More information